Chemical process simulators were first introduced to the
market in the late 1970s and early 1980s. Beginning with that
original modeling breakthrough, process engineering has
undergone significant transformation over the years, catalyzed
by advances and innovation in software, both within individual
disciplines and also in the integration across the workflow.
This continuous evolution has created tremendous value for many
companies, resulting in capital and energy savings, increased
safety and reliability, and optimized designs
with dramatic improvements in engineering quality and
productivity.

This article outlines specific innovations and best
practices, highlights examples of recent successes in the
industry, and examines new approaches that are presenting
additional opportunities for change in process engineering
practice. The examples offered here span the asset-creation
lifecycle, including R&D, early feasibility studies,
conceptual engineering, basic engineering, equipment design,
economic evaluation, energy management, debottlenecking and
continuous improvement, and engineering support to
manufacturing and planning.

A common aspect of all these examples is the huge impact
that cross-discipline integration of modeling and analysis
tools have on the selection of the best design options, the
overall quality of the designs, and the safe and profitable
operation of process plants. The ability to achieve superior
energy and environmental performance while, at
the same time, saving both capital and operating expense would
not be possible without the capability of the models to rapidly
analyze many alternative solutions and present design and cost
tradeoffs to decision-makers. This is critical, as companies
across the globe are facing an increasingly interconnected and
highly competitive environment.

Given their highly varied role, process engineers benefit from
interactions with other engineering and operational functions
and disciplines on a daily basis. The exchange of ideas,
recommendations, designs, analyses, plant data and process
models of various kinds support optimizing increasingly complex
designs and operations. By considering a broader range of
ideas, companies are able to achieve improved asset
performance, reduced costs, and increased safety and
reliability.

Unfortunately, within many organizations, communication
between engineering disciplines and with other functions is
manual, sequential and inefficient. Understanding of the
current state of the asset and opportunities for improvement
are not consistently shared and executed across the groups.
Organizational silos hold the expertise and data of
each group and discipline largely within the group; this
expertise is shared with other groups only after a design is
released, or on a case-by-case basis, when it is
important. This leads to significant loss of opportunitiesm,
since ideas developed in one group are not fully exploited by
other groups. Furthermore, the sequential nature of the
interactions between engineering groups limits the screening of
multiple alternatives during design and manufacturing, leading
to suboptimal choices.

This sequential workflow results in a significant loss of
opportunity in capital, energy and operating costs, and a
missed chance to improve safety and reliability, which amounts to
hundreds of billions of dollars per year in lost value.
Improving this information-sharing and workflow presents a
compelling opportunity to help asset owners and engineering
companies to actualize the lost opportunities through a clear
understanding of current asset performance and identification
of optimum improvement opportunities that can be consistently
executed across the lifecycle.

What follows is an examination of the journey of process
engineering over the last three decades and the exciting
innovations driving its evolution.

Impact of the desktop revolution

Following the introduction of process simulators (first on
mainframes and mini-computers), personal computer (PC)
price/performance was the next major breakthrough.

This had a dramatic impact on process engineering practice
in the 1980s and 1990s. It lowered the barrier-to-entry to
automate engineering calculations and democratized the ability
to model and analyze an asset through process simulation and
modeling.

The steady increase of PC power to solve bigger simulation
models quickly moved the simulators from mainframes to each
engineers PC. The evolving Microsoft Windows user
environment spurred an evolution in ease of use of the models
with graphical user interfaces, making them more accessible to
a broader range of chemical engineers.

This accessibility has enabled process engineers working on
plant problems to quickly establish an understanding of the
current asset performance and rapidly consider improvement
opportunities through the modeling of what-if
scenarios. Expanded access to plant data and to manufacturing
and planning tools helps process engineers translate
improvement ideas into real benefits for the asset owners.

However, the authors have observed that, despite all of
these democratizing trends, there is still a huge opportunity
for all organizations to take advantage of these powerful
modeling capabilities. To break through this barrier, we have
conducted extensive usability studies and introduced radically
new user interface concepts into several modeling tools, and we
are continuing to do so across the gamut of engineering
optimization products. Of this breakthrough user environment,
BP Chemicals Dr. Godwin Tongo reports, The new
paradigm has provided us with a big leap in flexibility and
ease of use, to enhance and optimize our engineering
productivity.1

Evolution of user interface and workflow paradigms has
continued to accelerate, catalyzed by new innovations in
software, hardware and mobile environments.

Convergence of modeling approaches

Another related area of improvement involves the convergence
of steady-state modeling with dynamic modeling tools and the integration of sequential modeling
with equation-oriented solution approaches. This has great
significance, with the time-consuming efforts to build dynamic
models and equation-oriented models for a complex process being
overcome through building models, first in the steady-state
mode and then reusing and building on them.

The ability to model processes dynamically is required to
address the increasing complexity of safety, startup and
quality challenges in highly optimized, large and integrated
process plants, as well as for effective modeling of sequential
batch units within process plants.

A recent example that demonstrates the power of this
approach is the use of dynamic modeling together with relief
system analysis tools for more accurate relief load and flare
system analysis. This results in significant savings in capital
costs related to flare capacity.2

Physical properties as innovation in process
optimization

Web and software evolutions have enabled several areas of
core chemical engineering innovation that provide the
foundation for process modeling and optimization. Such
innovations have been an integral part of achieving
optimization benefits.

Today, a large and expanding set of highly accurate
thermophysical properties is accessible to modelers. The
example of a close-boiling distillation column (Fig.
2) provides a clear picture of the value of better
thermophysical property characterization. A 5% error in
vapor-liquid equilibrium (VLE) predictions can result in 100%
error in capital cost estimation for the distillation column, which is a
major capital item. Therefore, accurate physical properties are
a key input parameter for reducing project capital and
technical risk.

Availability of physical properties data has always been a
challenge in developing a new process or equipment design.
Innovations in this area continue to accelerate engineering
efficiency while improving the accuracy and reliability of model predictions and
equipment sizing.

New optimization algorithms have continued to expand the
scope and impact of process optimization. Improved analysis and
visualization tools help engineers understand complex
phenomena, enabling the development of more efficient
processes. Continued focus on these innovations will be
critical for value creation through process optimization.

Optimizing engineering through collaboration

In addition to innovations in process engineering, another
aspect critical for value creation is collaboration among
groups and disciplines to consider cost and energy parameters
in the designs.

The traditional workflow for conceptual engineering today is
sequential. During conceptual engineering, the main objective
is to screen multiple design alternatives to ensure that an
optimum design has been selected. A process engineer typically
develops these alternatives using a process simulation tool.
The most promising alternatives are then passed on to equipment
(e.g., heat exchanger) specialists to size and design the
equipment. The equipment specialist develops preliminary
equipment designs and passes these to cost estimators. This
sequence of tasks may take several days or even weeks to
complete.

The sequential nature of the workflow slows down the overall
process and limits the number of design alternatives that can
be evaluated in the short window of opportunity available for
conceptual engineering. One consequence is that economics and
adequate equipment options are not considered early enough. The
result is suboptimal designs and lost opportunities.

Fig. 3 shows the integrated conceptual
engineering workflow that is now possible today. The integrated
approach provides access to equipment modeling, sizing and
economic analysis capabilities inside the simulation environment simultaneously, in a
manner that a process engineer can use without being a
specialist in design and estimating.

Fig. 3. Todays
integrated conceptual engineering workflow.

This integrated approach allows the process engineer to
rapidly screen multiple alternatives in a matter of hours
instead of days or weeks. This saves 10%30% capital and
energy compared to the traditional approach, since multiple
alternatives can be rapidly screened and designs can be
optimized early. BASF believes that its net benefits from the
broad use of process simulation and conceptual engineering, in
a comprehensive way, have been between 10% and 30% of installed
capital cost of projects.3

Additionally, Dow Chemical reported savings of $65 million
(MM) using integrated simulation and equipment modeling. The
approach enabled identification of a debottlenecking
opportunity in a chemical process while diagnosing and fixing a
specific operational problem.4

Another newly accessible integrated workflow enables
activated energy analysis directly from within the simulation
model, so that promising conceptual options for energy savings
can be identified during process design. Activated energy
analysis, with equipment and cost analysis, enables process
engineers to quickly identify the most promising options within
their familiar process simulation user environment.

Using this innovation, Huntsman Chemical reported a
reduction of 25% in energy intensity5; Honam Petrochemicals saw energy savings of
17.5% with the integrated conceptual engineering
approach6; and S-Oil reported savings of $39 MM with
payback of less than one year.7

Optimizing support to manufacturing and planning

A significant part of process engineering at operating
companies involves supporting manufacturing and supply chain
activities to troubleshoot and optimize assets. One of the key
challenges is that engineering, manufacturing and supply chain
teams do not share a common understanding of the current state
of the asset and opportunities for improvements. As a result,
initiatives to improve performance are often developed in silos
and, in some cases, they compete with each other.

The process engineers focus is on understanding the
process and predicting its performance. However, this is not
shared effectively with the stakeholders in the manufacturing
and supply chain. Communication tends to be ad hoc through a
variety of mechanisms including emails, Excel spreadsheets,
models, drawings and face-to-face meetings, among others. This
prevents complete alignment across the key disciplines and,
more importantly, results in lost opportunities for the asset
owners.

Today, one may reuse a process model of the asset for
what-if analysis, decision support and optimization of the
asset. A process model encapsulates knowledge of the asset and
provides the ability to reliably predict asset behavior. One
approach to enable plant personnel to access the models is to
use an Excel-based or real-time interface to shield model
complexity.

Another opportunity involves reuse of process modeling
information in production planning. This ensures that the
production plan is based on accurate information of the current
state of the operation and can correctly predict the
optimization potential. Another area of innovation is the
provision of real-time data for viewing and analysis within the
process model itself. This provides a one-stop shop
for troubleshooting operational issues.

Saudi Aramco has been using a combination of process models
and production planning modelsreferred to as the
Integrated Oil and Gas Modelto optimize exploration and
production assets. This model is used for daily optimization
and for planning purposes. Saudi Aramco has reported benefits
of a 3%8% increase in production, a 3%5% reduction
in energy usage, and a 50%70% decrease in planning
time.8

New learning paradigms

A large number of new engineers are joining the process
industry. This generation of engineers is rapidly changing the
composition of the process industry workforce. There are
considerable challenges in transferring an organizations
intellectual property and knowledge, which are tied up in
sophisticated models, to this new wave of engineers.

Discussions with key users have highlighted that, beyond the
use of software, learning is also integrally tied to becoming
experienced in discipline practices, such as developing
conceptual design, flare systems analysis, and capital project
estimating (among others); as well as in effectively training
organizations to use the integrated workflows correctly. The
software industry has responded to this need by introducing
effective search tools and online training for engineering
tools.

The prize for process optimization

An overview of the integrated process engineering workflow
achievable today is shown in Fig. 4. The
overall benefits of adopting a well-integrated process
engineering workflow are a 10%30% capital and operating
cost savings due to inherently better designs, a 10%20%
improvement in engineering quality, and a 10%20%
improvement in engineering efficiency. The integrated workflow
enables process optimization and complements innovations in
process engineering.

Process engineers play a key part in this workflow because
of their understanding of the process. Their ability to model
and optimize processes is at the core of value creation from
the entire integrated workflow throughout the asset
lifecyclefrom conceptual design to operations.

Considering the opportunities ahead

What are the key opportunities for process engineering? New
innovations will continue to broaden the scope for process
optimization through new, more accurate models for physical
properties and process equipment, and through new optimization
innovations. Further integration will enable process modelers
to have better vision in optimizing process schemas against
more parameters, including economics and sustainable
operations.

Standalone design and analysis, such as equipment sizing and
detailed equipment design, can be expected to play a more
prominent role in the simulation modeling world. Advanced
collaboration within engineering tools, combined with advances
in engineering databases, will provide opportunities to better
integrate global teams.

This journey is already beginning with the introduction of
new process modeling search tools. IT innovations such as
social networking, mobile and cloud computing platforms, and
search technologies will transform process engineering once
again.

Breakthroughs here can be expected to increase access to
process modeling tools, reduce the learning barrier, and make
optimization choices more visual and transparent. Web and cloud
innovations will integrate people in addition to facilitating
the integration of software applications. Process engineers
will play an even more important role in this transformation
due to their focus on understanding, modeling and optimizing
processes. HP

Dr. Vikas
Dhole joined Aspen Technology in 1997 and serves
as vice president of engineering product management. His
responsibilities include strategy, direction and business
performance of the aspenONE Process Engineering suite.
Dr. Dhole previously held a variety of leadership
positions in product management, technology development
and consulting services. Prior to joining Aspen
Technology, Dr. Dhole was technology manager with
Linnhoff March Ltd. UK (now part of KBC Ltd.) and a
lecturer at the Department of Process Integration at the
University of Manchester, UK. Throughout his career, Dr.
Dhole has championed innovative technology and software
solutions in the areas of conceptual design, energy
management and process engineering optimization. He has
authored several publications on these topics. Dr. Dhole
has a BTech degree in chemical engineering from the Indian Institute of Technology
(IIT), Mumbai, India, and a PhD in process integration from the
University of Manchester, UK.

Ron Beck
is engineering product marketing manager at Aspen
Technology. He has been with Aspen Technology for five
years and is the marketing manager for the aspenONE
Process Engineering suite. Mr. Beck spent 10 years in an
R&D organization that commercialized fluidized bed
technologies, enhanced oil recovery methods and environmental technology. He
has 20 years of experience in the development, adoption
and marketing of software solutions for engineering and
plant management.
He has also been involved with the development of
integrated solutions for several global chemical
enterprises, as well as Aspen Technologys economic
evaluation products. Mr. Beck is a graduate of Princeton
University.

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This a good example of low cost innovation in process engineering. I congratulate the authors for the article.Integration of process modeling and equipment design is infact being practised by Process licensors who would have a tie-up with an equipment vendor .This is a win-win situation for both. However for a design and engineering organisation this aproach may not be practical always. It is unlikely for the Engineering company to have state-of-the-art equipment design capability. Some companies do spend a lot to acquire such capability by becoming member of cooperative research organisations e.g. FRI, HTRI, UMIST etc. Also in equipment design lot of experience is used. However the approach is sound and need to be encouraged which would ensure low cost optimised design.

Hirak Dutta12.20.2012

Very well written article. I endorse that collaboration is the key. The communication process amongst the group of engineers viz. process engineers, instrumentation, mechanical, metallurgist is a must. A well structured meaningful interaction would undoubtedly help in achieving optimisation. Working in isolation on PCs do not serve the purpose. Further regular field visits and talking to the people at shop floor also provides lot of avenues for improvement.